1. technology and computing

SK Mastan Vali* et al.
ISSN: 2250-3676
[IJESAT] [International Journal of Engineering Science & Advanced Technology]
Volume-5, Issue-2, 065-069
MULTI-RESOLUTION SVD BASED INVERTIBLE DIGITAL IMAGE
WATERMARKING USING DUAL TREE COMPLEX WAVELET
TRANSFORM
SK. Mastan Vali1, G. Prathibha2
1
PG Student, Dept. of ECE, Acharya Nagarjuna University College of Engg. & Tech, A.P., India,
[email protected]
2
Assistant Professor, Dept. of ECE, Acharya Nagarjuna University College of Engg. & Tech, A.P., India,
[email protected]
Abstract
This paper proposes a new way of performing the image watermarking algorithm using Dual tree complex wavelet transform
based on the multi-resolution singular value decomposition method. The basic reason to choose multi-resolution singular value
decomposition (SVD) in combination with dual tree complex wavelet transform (DT-CWT) is due to its efficient and enriched
performance towards the common image attacks like Median and Gaussian. Also the multi-resolution SVD will enhance stability
in the singular values of an image. Here, the performance of watermarking is evaluated by considering the image quality metrics
like Root Mean Square Error (RMSE), and Peak Signal to Noise Ratio (PSNR).
Index Terms: Multi-Resolution SVD, Dual Tree Complex Wavelet Transform (DT-CWT), Root Mean Square Error
(RMSE), Peak Signal to Noise Ratio (PSNR).
--------------------------------------------------------------------- *** -----------------------------------------------------------------------1. INTRODUCTION
Now a day‟s data hiding is a challenging task in terms of
providing services to the particular authorised entities. In this
regards image watermarking plays an important role to
maintain authorship and rigid nature in the secured
information. The process of watermarking can be categorized
into two different ways that is visible or invisible
watermarking. In the visible watermarking situation the
watermark like data may be printed on the image or in the case
of invisible watermarking the watermark is merged into the
cover image, and it will appears as normal image to unauthorized persons. Several researches had shown their
contribution towards enhancing the robustness in authorised
information and also create interest in developing the new
algorithms for increasing the reliability. In this scenario, one
such approach is wavelets which cause revolutionary impact
on the image processing nevertheless the image watermarking.
Y. Wang et.al.[1] have discussed a non-blind digital image
watermarking algorithm based on discrete wavelet transform,
It results about 38.7744dB of PSNR and an amount of RMSE
is 8.6233. Merely in case of DWT, due to the process of down
IJESAT | Mar-Apr 2015
Available online @ http://www.ijesat.org
sampling during decomposition we loss the trueness of the
watermark image. The drawbacks in the DWT can be
overcome by using SWT, Elizabeth Chang et.al,[2], proposed
a method on SWT and it results a PSNR of 38.7847dB and
RMSE of 8.6231, but the problem with SWT is that it
considers all the redundant information in the image. This will
results more memory requirement. To overcome the weakness
of wavelets in higher dimensions, E. Ganic et.al.[3], proposed
a proposed an image watermarking technique based on DWTSVD, and it results PSNR of 39.3011 and the amount of
RMSE is 5.9702. C. Yuan at.el.[4] proposed a SWT-SVD
domain watermarking and it will results 39.4018dB and the
amount of RMSE is 8.0589. In this paper we proposed a
method to enhance the performance of multi-resolution
images watermarking named as Multi-resolution SVD based
invertible digital image watermarking using Dual Tree
Complex Wavelet Transform.
1.1 PROPOSED METHOD
The main drawback of discrete wavelet transform is its
inadequate directional sensitivity and also it shows negative
impact on shift invariance property. The reason behind to
65
SK Mastan Vali* et al.
ISSN: 2250-3676
[IJESAT] [International Journal of Engineering Science & Advanced Technology]
choose the complex wavelet transform is, it will solve the
problem of shift invariance in an effective manner.
Unfortunately we are unable to reconstruct the original
watermarked image, in order to fulfil this requirement the
concept dual tree was introduced into the existing one. Now
the dual tree complex wavelet transform perform the image
decomposition in an enriched manner. Moreover the
efficiency of the DT-CWT technique can be emphasized by
introducing the concept called singular value decomposition
(SVD). The purpose of choosing the SVD is that, it can
represent intrinsic algebraic properties in an effective manner
and also these singular values of an image boost up the
stability. Merely the image watermarking using DT-CWT
based on SVD impinges the performance of multi-resolution
images.
1.2 PROPOSED WATERMARKING ALGORITHM
i. Embedding the Watermark:
In the process of embedding the watermark image into a cover
image we utilize the DT-CWT and the multi-resolution SVD
for the sake of providing transparency. The process of
embedding the watermark is as shown in Figure1.
The transparency is achieved of efficiently decompose the
image with multi-resolutions, but normal SVD approach can‟t
meet such requirement. In order to achieve high degree of
security by maintaining each and every resolution in the image
that is going to be watermarked. Initially, perform image
resizing operation to both cover image and watermark image
to make sure that watermarked image should be appeared as
free from ilmage degradations. Then, apply image
decomposition process to obtain coefficients of the images
with the help of dual tree complex wavelet transform. These
coefficients are corresponding to the multi-resolution images.
In order to achieve stability in this multi-resolution image
coefficients singular value decomposition has to be applied;
moreover the multi-resolution SVD can cause the coefficients
of an image free from perturbations. These singular values
have to be combined in a prescribed manner that is by
selecting the appropriate singular value coefficients in both the
images. Let the approximate value for combining the singular
values is  factor, then proper selection this value will leads
to an efficient watermarked image. Then reconstruct the newly
formed image coefficients with the help of dual tree inverse
complex wavelet transform which results an watermarked
image with enriched stability.
IJESAT | Mar-Apr 2015
Available online @ http://www.ijesat.org
Volume-5, Issue-2, 065-069
Cover Image
Watermark Image
Image
Resizing
Image
Resizing
Image Decomposition
Image Decomposition
Dual Tree
Complex Wavelet
Transform
Dual Tree
Complex Wavelet
Transform
Sub-band Selection
Horizontal (or) Vertical
Sub-band Selection
Horizontal (or) Vertical
Apply Multi-resolution
SVD to the selected Subband Coefficients
Apply Multi-resolution
SVD to the selected Subband Coefficients
D
V
S
S
V
D
Combine S
coefficients
based on 
factor
Fuse the S, V and D coefficients
New Sub-band
Horizontal (or) Vertical
was generated
Image Reconstruction
Inverse Dual Tree
Complex Wavelet
Transform
Watermarked
ii. Extracting the Watermark:
Image
Figure1. Process of Embedding the Watermark
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SK Mastan Vali* et al.
ISSN: 2250-3676
[IJESAT] [International Journal of Engineering Science & Advanced Technology]
On the other hand, in order to recover back the watermark
image from the watermarked image, we need to abide by the
following procedure. The watermarked image is an image
which inherits the confidential information; the information
may be either a picture or a digital data. Now in order to
obtain the hidden information, first we need to convert the
spatial domain values into transform domain by using dual
tree complex wavelet transform such procedure in named as
image decomposition. The decomposed coefficients are
applied to multi-resolution SVD for the sake of obtaining the
singular values of the image; these singular values are
corresponding to the watermarked image. Now the  factor
plays vital role in categorizing the coefficients that are
corresponds to the watermark image, by using this value the
watermark image can be recovered back effectively from the
watermarked image. These transformed domain watermark
image coefficients are then converted into spatial domain with
the help of dual tree inverse complex wavelet transform. The
process of extracting the watermark is as shown in Figure2.
Watermarking
Technique
Volume-5, Issue-2, 065-069
RMSE
PSNR
8.6233
38.7744
SWT
8.6231
38.7847
DWT-SVD
5.9702
39.3011
SWT-SVD
8.0589
39.4018
PROPOSED
METHOD
3.9917
45.8613
DWT
Watermarked Image
Table1. Comparison of various watermarking techniques with
the proposed method
Image Decomposition
3. GRAPHICAL REPRESENTATIONSOF VARIOUS
WATERMARKING PARAMETERS
Dual Tree Complex Wavelet
Transform
10
9
RMSE
8
Apply Multi-resolution SVD
V
S
D
7
6
5
4
Extract the
coefficients of S
based on  factor
3
2
1
0
Image Reconstruction
Inverse Dual Tree Complex
Wavelet Transform
Figure3. Comparison of RMSE for various watermarking
approaches
2. COMPARISION OF VARIOUS WATERMARKING
Watermark Image
TECHNIQUES
Figure2. Process of Extracting the Watermark
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67
SK Mastan Vali* et al.
ISSN: 2250-3676
[IJESAT] [International Journal of Engineering Science & Advanced Technology]
Volume-5, Issue-2, 065-069
50
PSNR
48
46
44
42
40
5. CONCLUSION
38
In this paper a new way of performing image watermarking
technique was proposed to safeguard the confidential data
with the help of DT-CWT and multi-resolution SVD. With
this approach the images with multi-resolution can be
watermarked effectively and it can be extracted efficiently as
compared to the other methods like Discrete wavelet
transform (DWT), Stationary wavelet transform (SWT),
DWT-SVD, SWT-SVD in terms of Root mean square error
(RMSE) and Peak signal noise ratio (PSNR).
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REFERENCES
Figure4. Comparison of PSNR for various watermarking
approach
4. SIMULATION RESULTS
Cover Image
Watermark Image
[1]. Y. Wang, J. F. Doherty and R. E. Van Dyck, “A Wavelet-Based
Watermarking Algorithm for Ownership Verification of Digital
Images”, IEEE Transactions on Image Processing, Volume 11,
No. 2, February 2002, pp. 77-88.
[2]. Potdar, Vidyasagar M., Song Han, and Elizabeth Chang. „„A
survey of digital image watermarking techniques‟‟. Industrial
Informatics, 2005. INDIN'05. 2005 3rd IEEE International
Conference on. IEEE, 2005.
[3]. E. Ganic and A. M. Eskicioglu, “Robust DWT-SVD Domain
Image Watermarking: Embedding Data in All Frequencies”,
13thEuropean Signal Processing Conference (EUSIPCO 2005),
Antalya, Turkey, September 4-8, 2005.
[4]
Watermarked Image
Q. Li, C. Yuan, and Y. Zhong, “Adaptive SWT-SVD Domain
Image
Watermarking
Using
Human
Visual
Model,”
proceedings of 9th international conference on advanced
communication Technology, vol. 3, pp. 1947- 1951, Feb.2007.
[5]. Ben Wang , Jinkou Ding , Qiaoyan Wen , Xin Liao , Cuixiang
Liu “An Image Watermarking Algorithm Based On DWT DCT
And SVD “Proceedings of IC-NIDC2009, 978-1-4244-49002/09/$25.00 ©2009 IEEE.pp.1034-1038.
Extracted Watermark Image
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Available online @ http://www.ijesat.org
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SK Mastan Vali* et al.
ISSN: 2250-3676
[IJESAT] [International Journal of Engineering Science & Advanced Technology]
[6]. Liu Liang and Sun Qi, “A new SVD-DWT composite
watermarking”, ICSP proceedings of IEEE International
conference on signal processing, 2006.
[7]. Ke-feng He,“Watermarking for images using the HVSand SVD
in the wavelet domain”, Proceedings of IEEE International
Conference on Mechatronics and Automation, pp. 2352-2356,
2006.
[8]. Jiang Xuehua, Digital Watermarking and Its Application in
Image Copyright Protection, International Conference on
Intelligent Computation Technology and Automation, Sch. of
Eng., Linyi Normal Univ., Linyi, China, pp. 114 – 117, 2010.
[9]. Xiong-Bo Zheng , Xiao-Wei Zhang, A Multiwavelet Based
Digital Watermarking Algorithm Using Texture Measures,
International Conference on Wavelet Analysis and Pattern
Recognition, Coll. Of Sci., Harbin Eng. Univ., Harbin, China,
pp. 260 – 265, 2010.
[10]. Selesnick I W, Baraniuk R G, Kingsbury N G 2005 The dualtree complex wavelet transform. IEEE Signal Processing
Magazine 22(6): 123–151
Volume-5, Issue-2, 065-069
Clustering and Enhanced Pixel-Wise Masking”, IEEE Trans.
on Image Processing, vol. 21, no. 8, Aug. 2012, pp. 3598-3611.
BIOGRAPHIES
Mr.
SK.
Diploma
MASTAN
in
Instrumentation
Applied
VALI
obtained
Electronics
Engineering
in
and
Govt.
Polytechnic for Minorities, Guntur in the
year 2009. B.Tech degree in Electronics and
Instrumentation Engineering from AHCET,
JNTU Hyderabad,in the year 2012. Presently he is pursuing M.Tech
in Communication Engineering and Signal Processing from Acharya
Nagarjuna University College of Engineering and Technology. His
interesting fields are Image Processing and
Control System
Mrs. G. PRATHIBHA obtained B.Tech
degree
from
RVR&JC
College
of
Engineering in the year 2005. M.Tech from
JNTU Hyderabad in the year 2007.
[11]. H Nyeem, W Boles, C Boyd, On the robustness and security of
Currently she is working as Assistant Professor in Acharya
digital image watermarking. Proceedings of ICIEV (IEEE,,
Nagarjuna University College of Engineering And Technology,
Piscataway, 2012), pp. 1136–1141
Guntur, A.P, INDIA. Her interesting fields are Pattern Recognition,
[12]. G. Plonka, Easy path wavelet transform: a new adaptive
Image Processing and Signal Processing
wavelet transform for sparse representation of two-dimensional
data, Multiscale Model. Simul., 2009.
[13]. Chittaranjan Pradhan, Shibani Rath, Ajay Kumar Bisoi, “Non
Blind Digital Watermarking Technique Using DWT and Cross
Chaos”, 2nd International Conference on Communication,
Computing & Security [ICCCS-2012], 2012, Vol. 6, pp 897–
904.
[14]. C. C. Chang and P. Tsai, “SVD-based Digital Image
Watermarking Scheme”, Pattern Recognition Letters, vol. 26,
pp. 1577-1586, 2005.
[15]. Lingling An, Xinbo Gao, Xuelong Li, Dacheng Tao, Cheng
Deng, and Jie Li,” Robust Reversible Watermarking via
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Available online @ http://www.ijesat.org
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